Effects of Nitrogen Accumulation, Transportation, and Grain Nutritional Quality and Advances in Fungal Endophyte Research in Quinoa (Chenopodium quinoa Willd.) Plants.

This study aims to understand the influence of nitrogen accumulation, fungal endophyte, yield, nitrogen use efficiency, and grain nutritional quality parameters on the yield of quinoa in some areas of China. The endophytic microbial community in plants plays a crucial role in plant growth, development, and health, especially in quinoa plants under different nitrogen fertilizer levels. The results from the present study indicated that appropriate nitrogen application significantly enhanced the nitrogen accumulation and yield of quinoa grains during maturity, increasing by 34.54-42.18% and 14.59-30.71%, respectively. Concurrently, protein content, amylose, total starch, ash, and fat content also increased, with respective growth rates of 1.15-18.18%, 30.74-42.53%, 6.40-12.40%, 1.94-21.94%, and 5.32-22.22%. Our constructed interaction network of bacterial and fungal communities revealed that bacteria outnumbered fungi significantly, and most of them exhibited synergistic interactions. The moderate increase in N150 was beneficial for increasing quinoa yield, achieving nitrogen use efficiency (NUE) of over 20%. The N210 was increased, and both the yield and NUE significantly decreased. This study provides novel insights into the impact of nitrogen fertilizer on quinoa growth and microbial communities, which are crucial for achieving agricultural sustainable development.

ARBUR, a machine learning-based analysis system for relating behaviors and ultrasonic vocalizations of rats.

Deciphering how different behaviors and ultrasonic vocalizations (USVs) of rats interact can yield insights into the neural basis of social interaction. However, the behavior-vocalization interplay of rats remains elusive because of the challenges of relating the two communication media in complex social contexts. Here, we propose a machine learning-based analysis system (ARBUR) that can cluster without bias both non-step (continuous) and step USVs, hierarchically detect eight types of behavior of two freely behaving rats with high accuracy, and locate the vocal rat in 3-D space. ARBUR reveals that rats communicate via distinct USVs during different behaviors. Moreover, we show that ARBUR can indicate findings that are long neglected by former manual analysis, especially regarding the non-continuous USVs during easy-to-confuse social behaviors. This work could help mechanistically understand the behavior-vocalization interplay of rats and highlights the potential of machine learning algorithms in automatic animal behavioral and acoustic analysis.

Shifting Redox Reaction Equilibria on Demand Using an Orthogonal Redox Cofactor.

Natural metabolism relies on chemical compartmentalization of two redox cofactors, NAD+ and NADP+, to orchestrate life-essential redox reaction directions. However, in whole cells the reliance on these canonical cofactors limits flexible control of redox reaction direction as these reactions are permanently tied to catabolism or anabolism. In cell-free systems, NADP+ is too expensive in large scale. We have previously reported the use of nicotinamide mononucleotide, (NMN+) as a low-cost, noncanonical redox cofactor capable of specific electron delivery to diverse chemistries. Here, we present Nox Ortho, an NMNH-specific water-forming oxidase, that completes the toolkit to modulate NMNH/NMN+ ratio. This work uncovers an enzyme design principle that succeeds in parallel engineering of six butanediol dehydrogenases as NMN(H)-orthogonal biocatalysts consistently with a 103 - 106 -fold cofactor specificity switch from NAD(P)+ to NMN+. We combine these to produce chiral-pure 2,3-butanediol (Bdo) isomers without interference from NAD(H) or NADP(H) in vitro and in E. coli cells. We establish that NMN(H) can be held at a distinct redox ratio on demand, decoupled from both NAD(H) and NADP(H) redox ratios in vitro and in vivo.

Diagnosis of Chest Pneumonia with X-ray Images Based on Graph Reasoning.

Pneumonia is an acute respiratory infection that affects the lungs. It is the single largest infectious disease that kills children worldwide. According to a 2019 World Health Organization survey, pneumonia caused 740,180 deaths in children under 5 years of age, accounting for 14% of all deaths in children under 5 years of age but 22% of all deaths in children aged 1 to 5 years. This shows that early recognition of pneumonia in children is particularly important. In this study, we propose a pneumonia binary classification model for chest X-ray image recognition based on a deep learning approach. We extract features using a traditional convolutional network framework to obtain features containing rich semantic information. The adjacency matrix is also constructed to represent the degree of relevance of each region in the image. In the final part of the model, we use graph inference to complete the global modeling to help classify pneumonia disease. A total of 6189 children's X-ray films containing 3319 normal cases and 2870 pneumonia cases were used in the experiment. In total, 20% was selected as the test data set, and 11 common models were compared using 4 evaluation metrics, of which the accuracy rate reached 89.1% and the F1-score reached 90%, achieving the optimum.

Orthogonal glycolytic pathway enables directed evolution of noncanonical cofactor oxidase.

Noncanonical cofactor biomimetics (NCBs) such as nicotinamide mononucleotide (NMN+) provide enhanced scalability for biomanufacturing. However, engineering enzymes to accept NCBs is difficult. Here, we establish a growth selection platform to evolve enzymes to utilize NMN+-based reducing power. This is based on an orthogonal, NMN+-dependent glycolytic pathway in Escherichia coli which can be coupled to any reciprocal enzyme to recycle the ensuing reduced NMN+. With a throughput of >106 variants per iteration, the growth selection discovers a Lactobacillus pentosus NADH oxidase variant with ~10-fold increase in NMNH catalytic efficiency and enhanced activity for other NCBs. Molecular modeling and experimental validation suggest that instead of directly contacting NCBs, the mutations optimize the enzyme's global conformational dynamics to resemble the WT with the native cofactor bound. Restoring the enzyme's access to catalytically competent conformation states via deep navigation of protein sequence space with high-throughput evolution provides a universal route to engineer NCB-dependent enzymes.

Image Classification Method Based on Multi-Agent Reinforcement Learning for Defects Detection for Casting.

A casting image classification method based on multi-agent reinforcement learning is proposed in this paper to solve the problem of casting defects detection. To reduce the detection time, each agent observes only a small part of the image and can move freely on the image to judge the result together. In the proposed method, the convolutional neural network is used to extract the local observation features, and the hidden state of the gated recurrent unit is used for message transmission between different agents. Each agent acts in a decentralized manner based on its own observations. All agents work together to determine the image type and update the parameters of the models by the stochastic gradient descent method. The new method maintains high accuracy. Meanwhile, the computational time can be significantly reduced to only one fifth of that of the GhostNet.

Bayesian Network Structure Learning Method Based on Causal Direction Graph for Protein Signaling Networks.

Constructing the structure of protein signaling networks by Bayesian network technology is a key issue in the field of bioinformatics. The primitive structure learning algorithms of the Bayesian network take no account of the causal relationships between variables, which is unfortunately important in the application of protein signaling networks. In addition, as a combinatorial optimization problem with a large searching space, the computational complexities of the structure learning algorithms are unsurprisingly high. Therefore, in this paper, the causal directions between any two variables are calculated first and stored in a graph matrix as one of the constraints of structure learning. A continuous optimization problem is constructed next by using the fitting losses of the corresponding structure equations as the target, and the directed acyclic prior is used as another constraint at the same time. Finally, a pruning procedure is developed to keep the result of the continuous optimization problem sparse. Experiments show that the proposed method improves the structure of the Bayesian network compared with the existing methods on both the artificial data and the real data, meanwhile, the computational burdens are also reduced significantly.

Tunnel manganese oxides prepared using recovered LiMn2O4 from spent lithium-ion batteries: Co adsorption behavior and mechanism.

The purpose of this study was to investigate Cobalt (Co) removal from wastewater using synthesized manganese oxides from the recovered LiMn2O4. An efficient ultrasonication leaching method was utilized to recycle LiMn2O4 from spent lithium-ion batteries (LIBs). The recovered LiMn2O4 was used to synthesize tunnel λ-MnO2, γ-MnO2 and ß-MnO2 by acid leaching and hydrothermal methods. Meanwhile, Li+ in the supernatant was recycled by the precipitation of Li3PO4. Subsequently, for the synthesized tunnel MnO2, various characterizations and sodium hydroxide titration in NaNO3 solution were performed. The effect of sorption studies presented the uptake of Co increased with the pH increasing from pH ~1 to pH ~8 and the isothermal sorption at pH ~6 showed that γ-MnO2 possessed the highest uptake amount 0.44 meq/g, and the highest distribution coefficient 2.5 × 105 mL/g. Moreover, γ-MnO2 was found without Mn3+/Mn2+ leached during the sorption process. The ion exchange-surface complexation model was adopted to study the titration, effect of pH and isotherm sorption on the ion exchange reaction mechanism of Co adsorption. Overall, this work provides an economically feasible and environmentally friendly method to recycle the spent LIBs and the γ-MnO2 synthesized from the recovered LiMn2O4 was proved to be promising adsorbents for Co removal.

Leveraging Oxidative Stress to Regulate Redox Balance-Based, In Vivo Growth Selections for Oxygenase Engineering.

Directed evolution methods based on high-throughput growth selection enable efficient discovery of enzymes with improved function in vivo. High-throughput selection is particularly useful when engineering oxygenases, which are sensitive to structural perturbations and prone to uncoupled activity. In this work, we combine the principle that reactive oxygen species (ROS) produced by uncoupled oxygenase activity are detrimental to cell fitness with a redox balance-based growth selection method for oxygenase engineering that enables concurrent advancement in catalytic activity and coupling efficiency. As a proof-of-concept, we engineered P450-BM3 for degradation of acenaphthene (ACN), a recalcitrant environmental pollutant. Selection of site-saturation mutagenesis libraries in E. coli strain MX203 identified P450-BM3 variants GVQ-AL and GVQ-D222N, which have both improved coupling efficiency and catalytic activity compared to the starting variant. Computational modeling indicates that the discovered mutations cooperatively optimize binding pocket shape complementarity to ACN, and shift the protein's conformational dynamics to favor the lid-closed, catalytically competent state. We further demonstrated that the selective pressure on coupling efficiency can be tuned by modulating cellular ROS defense mechanisms.

On the challenges of greyscale-based quantifications using X-ray computed microtomography.

For X-ray computed microtomography (µ-CT) images of porous rocks where the grains and pores are not fully resolved, the greyscale values of each voxel can be used for quantitative calculations. This study addresses the challenges that arise with greyscale-based quantifications by conducting experiments designed to investigate the sources of error/uncertainty. We conduct greyscale-based calculations of porosity, concentration and diffusivity from various µ-CT experiments using a Bentheimer sandstone sample. The dry sandstone is imaged overtime to test the variation of greyscale values over sequential scans due to instrumentation stability. The sandstone is then imaged in a dry and contrast-agent saturated state at low resolution to determine a porosity map, which is compared to a porosity map derived from segmented high-resolution data. Then the linearity of the relationship between the concentration of a contrast agent and its corresponding attenuation coefficient is tested by imaging various solutions of known concentration. Lastly, a diffusion experiment is imaged at low resolution under dynamic conditions to determine local diffusivity values for the sandstone, which is compared to values derived from direct pore-scale simulations using high-resolution data. Overall, we identify the main errors associated with greyscale-based quantification and provide practical suggestions to alleviate these issues. LAY DESCRIPTION: X-ray computed microtomography (CT) imaging has become an important way to study the pore space of a porous medium. Using segmented images, we can build 3D pore space models for porous media and characterize the morphology and/or run simulations on the models. So, image segmentation is a critical image processing step. However, for low resolution images where image segmentation is not possible, grayscales are directly used for quantifications such as porosity and concentration calculations. Although these types of calculations have been widely accepted and used, the uncertainties and errors associated with grayscale-based quantifications are not fully discussed. Here we specifically design experiments with X-ray CT imaging to address the challenges that arise in grayscale-based quantifications. For instance, in order to validate porosity calculation results from low resolution images (with the help of high attenuating tracer), high resolution images are also acquired, which serve as a benchmark. The errors associated with concentration calculation using grayscale values are also discussed. In addition, numerical simulations using grayscale values are performed on a diffusion experiment images with X-ray CT. The problems that arise in dynamic imaging and the subsequent numerical simulations are discussed. The experiments, calculations and discussions provide a more comprehensive understanding on grayscale-based quantifications and aid in designing better X-ray CT experiments.

A unique activation-promotion mechanism of the influenza B M2 proton channel uncovered by multiscale simulations.

The influenza B M2 protein (BM2) forms an acid-activated proton channel that is important for the virus's lifecycle. Despite extensive research efforts, the detailed activation mechanism of the BM2 proton channel is often elusive. Herein a pH-regulated mechanism of the BM2TM domain has been systematically characterized using multiscale computer simulations, including classical molecular dynamics, constant pH molecular dynamics (CpHMD) and quantum mechanics/molecular mechanics (QM/MM) approaches. Our simulations reveal a pH-dependent conformational switch from the C-terminal closed to the C-terminal open conformers, and provide the free energy of conformational activation coupled to the titration of the His19 tetrad. Importantly, our results confirm the coupling titration between the His19 tetrad and His27 tetrad, and identify that the full-cationic state (His2744+) dominates at the low pH (the His19 tetrad at +2, +3 and +4 charge states). Our QM/MM simulations indicate that the second titratable histidine, His27, could further promote the BM2 acid activation and speed up proton dissociation from the HxxxW motif, thus facilitating proton conduction by BM2. Taken together, a unique "activation-promotion mechanism" about the BM2 proton channel is proposed, and these results may be helpful for the understanding of other similar proton channels and the development of BM2 inhibitors.

How Chorismatases Regulate Distinct Reaction Channels in a Single Conserved Active Pocket: Mechanistic Analysis with QM/MM (ONIOM) Investigations.

The FkbO and Hyg5 subfamilies of chorismatases share the same active-site architectures, but perform distinct reaction mechanisms, that is, FkbO employs a hydrolysis reaction whereas Hyg5 proceeds through an intramolecular mechanism. Despite extensive research efforts, the detailed mechanism of the product selectivity in chorismatases need to be further unmasked. In this study, the effects of the A/G residue group (A244FkbO /G240Hyg5 ) and the V/Q residue group (V209FkbO /Q201Hyg5 ) on the catalytic mechanisms are investigated by employing molecular dynamics simulations and hybrid quantum mechanical/molecular mechanical (QM/MM) calculations of the two wild-type models (FkbO/CHO and Hyg5/CHO; CHO=chorismate) and four mutants models (A244G-FkbO/CHO and G240A-Hyg5/CHO; V209Q-FkbO/CHO and Q201V-Hyg5/CHO). Our results showed that the A/G residue group mentioned by previous works would cause changes in the binding states of the substrate and the orientation of the catalytic glutamate, but only these changes affect the product selectivity in chorismatases limitedly. Interestingly, the distal V/Q residue group, which determines the internal water self-regulating ability at the active site, has significant impact on the selectivity of the catalytic mechanisms. The V/Q residue group is suggested to be an important factor to control the catalytic activities in chorismatases. The results are consistent with biochemical and structural experiments, providing novel insight into the mechanism of product selectivity in chorismatases.

What regulates the catalytic activities in AGE catalysis? An answer from quantum mechanics/molecular mechanics simulations.

The AGE superfamily (AGEs) is made up of kinds of isomerase which are very important both physiologically and industrially. One of the most intriguing aspects of AGEs has to do with the mechanism that regulates their activities in single conserved active pocket. In order to clarify the relationship among single conserved active pocket and two activities in AGEs, results for the epimerization activity catalyzed by RaCE and the isomerization activity catalyzed by SeYihS were obtained by using QM/MM umbrella sampling simulations and 2D-FES calculations. Our results show that both of them have similar enzyme-substrate combination mode for inner pyranose ring in single conserved active pocket even though they have different substrate specificity. This means that the pathways of ring opening catalyzed by them are similar. However, one non-conserved residue (Leu183 in RaCE, Met175 in SeYihS) in the active site, which has different steric hindrance, causes a small but effective change in the direction of ring opening in stage 1. And then this change will induce a fundamentally different catalytic activity for RaCE and SeYihS in stage 2. Our results give a novel viewpoint about the regulatory mechanism between CE and YihS in AGEs, and may be helpful for further experiments of rational enzyme design based on the (α/α)6-barrel basic scaffold.